Three Key Considerations When Implementing AI in Drug Discovery

DATE

October 6, 2022

TIME

8:00 a.m. PDT, 11:00 a.m. EDT, 16:00 BST, 17:00 CEST

Duration: 60 Minutes

Overview

Despite the buzz around artificial intelligence (AI), most industry insiders know that the use of machine learning (ML) in drug discovery is nothing new. For more than a decade, researchers have used computational techniques for many purposes, such as finding hits, modeling drug-protein interactions, and predicting reaction rates.

What is new is the hype. As AI has taken off in other industries, countless start-ups have emerged promising to transform drug discovery and design with AI-based technologies. While a few “AI-native” candidates are in clinical trials, around 90% remain in discovery or preclinical development, so it will take years to see if the bets pay off. This begs the question: Is AI for drug discovery more hype than hope? Absolutely not. Do we need to adjust our expectations and position for success? Absolutely, yes. In this webinar we will discuss Three Keys to Implementing AI in Drug Discovery Implementing AI in drug discovery requires: reasonable expectations, clean data, and collaboration.

Key Learning Objectives:

Reasonable expectations of AI
Readiness for AI

Who Should Attend:

Cheminformatics leaders
Cheminformatics scientists
Drug discovery leaders
Drug discovery scientists

Brought to you by:

Speakers

Haydn Boehm
Director of Product Marketing
Dotmatics

Melissa O'Meara
Forensic Science Consultant
C&EN Media Group

Registration

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